Hybrid Methods in Pattern Recognition

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The field of pattern recognition has seen enormous progress since its beginnings. A large number of different approaches have been proposed and hybrid methods aim at combining the advantages of different paradigms within a single system. This collection of articles describes progress in this emerging field. It covers topics such as the combination of neural nets with fuzzy systems or hidden Markov models, neural networks for the processing of symbolic data structures, hybrid methods in data mining, the combination of symbolic and subsymbolic learning, and others. Also included is work on multiple classifier systems. Furthermore, the work deals with applications in on-line and off-line handwriting recognition, remotely-sensed image interpretation, fingerprint identification, and automatic text categorization.